Instructions to use dogman189/odysseus-2-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dogman189/odysseus-2-chat with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="dogman189/odysseus-2-chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dogman189/odysseus-2-chat")
model = AutoModelForCausalLM.from_pretrained("dogman189/odysseus-2-chat", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dogman189/odysseus-2-chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dogman189/odysseus-2-chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dogman189/odysseus-2-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dogman189/odysseus-2-chat

SGLang

How to use dogman189/odysseus-2-chat with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "dogman189/odysseus-2-chat" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dogman189/odysseus-2-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "dogman189/odysseus-2-chat" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dogman189/odysseus-2-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dogman189/odysseus-2-chat with Docker Model Runner:
```
docker model run hf.co/dogman189/odysseus-2-chat
```

Odysseus 2 Chat

A chat-tuned version of Odysseus 2, fine-tuned with LoRA on the Alpaca-cleaned dataset (51,760 examples).

Model Details

Base model: dogman189/odysseus-2
Fine-tuning: LoRA (rank 8, alpha 16) on Alpaca-cleaned
Architecture: GPT-2 (GPT2LMHeadModel)
Parameters: ~320M
Context length: 512 tokens
Vocabulary: 16,000 BPE tokens

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "dogman189/odysseus-2-chat"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "User: What is 2+2?\n\nAssistant:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Chat Format

The model was fine-tuned on the format:

User: {instruction}

Assistant: {response}

Training Details

LoRA fine-tune of the base model on alpaca-cleaned (51,760 examples)
LoRA rank 8, alpha 16, dropout 0.1, targeting c_proj, c_fc, c_attn
6 epochs (default)

Limitations

Small model fine-tuned on a limited chat dataset. Responses may be inaccurate or nonsensical. Not intended for production use.

Downloads last month: 355

Safetensors

Model size

0.3B params

Tensor type

F32

Model tree for dogman189/odysseus-2-chat

Base model

dogman189/odysseus-2

Adapter

(1)

this model